Systems and methods for updating generic file-classification definitions

ABSTRACT

A computer-implemented method for updating generic file-classification definitions may include (1) identifying at least one generic file-classification definition deployed in a software product installed on a client device, (2) classifying at least one data sample encountered by the client device based at least in part on the generic file-classification definition, (3) querying at least one verification server in an attempt to verify the correctness of the classification of the data sample, (4) determining that the classification of the data sample is incorrect based at least in part on the query, and then (5) modifying the generic file-classification definition deployed in the software product based at least in part on the data sample. Various other methods, systems, and computer-readable media are also disclosed.

BACKGROUND

Generic file-classification definitions are often used to classify filesbased at least in part on the files' features. For example, a securitysoftware product may apply a generic file-classification definition to afile encountered by an end user's computing device. In this example, thesecurity software product may compare various features of the file (suchas the file's name, size, storage location, source, extension, format,and/or creation date) with the generic file-classification definition.By comparing such features with the generic file-classificationdefinition, the security software product may be able to fairlyaccurately classify the file as either clean or malicious.

Unfortunately, such generic file-classification definitions may stilllead to false positives and/or false negatives in certain scenarios. Forexample, a security software vendor may generate the genericfile-classification definition from a set of training data that includesknown clean and/or malicious files. However, after generating thegeneric file-classification definition and releasing the same to thesecurity software product, the security software vendor may identify newclean and/or malicious files. Since the set of training data did notinclude these newly identified files, the generic file-classificationdefinition may fail to account for certain information derived fromthese newly identified files. As a result, the genericfile-classification definition may cause the security software productto produce a false negative and/or false positive upon encountering oneof these files on the end-user's computing device.

The instant disclosure, therefore, identifies and addresses a need forimproved systems and methods for updating generic file-classificationdefinitions to account for newly identified clean and/or maliciousfiles.

SUMMARY

As will be described in greater detail below, the instant disclosuredescribes various systems and methods for updating genericfile-classification definitions to account for newly identified cleanand/or malicious files.

In one example, a computer-implemented method for updating genericfile-classification definitions may include (1) identifying at least onegeneric file-classification definition deployed in a software productinstalled on a client device, (2) classifying at least one data sampleencountered by the client device based at least in part on the genericfile-classification definition, (3) querying at least one verificationserver in an attempt to verify the correctness of the classification ofthe data sample, (4) determining that the classification of the datasample is incorrect based at least in part on the query, and then (5)modifying the generic file-classification definition deployed in thesoftware product based at least in part on the data sample.

In one example, the method may also include identifying at least onedata cluster that includes a plurality of training data samples, acentroid, and/or a distance threshold. In this example, the method mayfurther include computing a distance from the centroid of the datacluster to the data sample. Additionally or alternatively, the methodmay include classifying the data sample based at least in part on thedistance from the centroid to the data sample.

In one example, the method may also include applying a distance functionthat generates a value representing the distance from the centroid todata sample. In this example, the method may further include determiningthat the value representing the distance from the centroid to the datasample is below the distance threshold by comparing the value with thedistance threshold. Additionally or alternatively, the method mayinclude determining that the value representing the distance from thecentroid to the data sample is above the distance threshold by comparingthe value with the distance threshold.

In one example, the method may also include determining that thedistance from the centroid to the data sample is above the distancethreshold. In this example, the method may further include increasingthe distance threshold such that the distance from the centroid to thedata sample is below the distance threshold.

In one embodiment, the distance threshold may represent a distance abovewhich data samples are unlikely to include malware. In this embodiment,the method may also include classifying the data sample as non-malwaredue at least in part to the distance from the centroid to the datasample being above the distance threshold. Additionally oralternatively, the method may include increasing the distance thresholdbeyond the data sample such that the generic file-classificationdefinition classifies the data sample as malware.

In one example, the method may also include determining that thedistance from the centroid to the data sample is below the distancethreshold. In this example, the method may further include decreasingthe distance threshold such that the distance from the centroid to thedata sample is above the distance threshold.

In one embodiment, the distance threshold may represent a distance belowwhich data samples are likely to include malware. In this embodiment,the method may also include classifying the data sample as malware dueat least in part to the distance from the centroid to the data samplebeing below the distance threshold. Additionally or alternatively, themethod may include decreasing the distance threshold within the datasample such that the generic file-classification definition classifiesthe data sample as non-malware.

In one embodiment, the centroid may include a reference data pointcalculated based at least in part on the plurality of training datasamples. Similarly, the distance threshold may include a referencedistance determined based at least in part on the plurality of trainingdata samples.

In one example, the method may also include classifying a plurality ofdata samples encountered by the client device based at least in part onthe generic file-classification definition. In this example, the methodmay further include customizing the generic file-classificationdefinition to the client device based at least in part on the pluralityof data samples encountered by the client device.

In one example, the method may also include obtaining information aboutthe data sample that was unavailable when the genericfile-classification definition was released. In this example, the methodmay further include determining that the classification of the datasample is incorrect based at least in part on the information about thedata sample.

In one example, a system for implementing the above-described method mayinclude (1) an identification module that identifies at least onegeneric file-classification definition deployed in a software productinstalled on a client device, (2) a classification module thatclassifies at least one data sample encountered by the client devicebased at least in part on the generic file-classification definition,(3) a query module that queries at least one verification server in anattempt to verify the correctness of the classification of the datasample, (4) a determination module that determines that theclassification of the data sample is incorrect based at least in part onthe query, and (5) a modification module that modifies the genericfile-classification definition deployed in the software product based atleast in part on the data sample. The system may also include at leastone processor configured to execute the identification module, theclassification module, the query module, the determination module, andthe modification module.

In one example, the above-described method may be encoded ascomputer-readable instructions on a computer-readable-storage medium.For example, a computer-readable-storage medium may include one or morecomputer-readable instructions that, when executed by at least oneprocessor of a computing device, may cause the computing device to (1)identify at least one generic file-classification definition deployed ina software product installed on a client device, (2) classify at leastone data sample encountered by the client device based at least in parton the generic file-classification definition, (3) query at least oneverification server in an attempt to verify the correctness of theclassification of the data sample, (4) determine that the classificationof the data sample is incorrect based at least in part on the query, andthen (5) modify the generic file-classification definition deployed inthe software product based at least in part on the data sample.

Features from any of the above-mentioned embodiments may be used incombination with one another in accordance with the general principlesdescribed herein. These and other embodiments, features, and advantageswill be more fully understood upon reading the following detaileddescription in conjunction with the accompanying drawings and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate a number of exemplary embodimentsand are a part of the specification. Together with the followingdescription, these drawings demonstrate and explain various principlesof the instant disclosure.

FIG. 1 is a block diagram of an exemplary system for updating genericfile-classification definitions.

FIG. 2 is a block diagram of an additional exemplary system for updatinggeneric file-classification definitions.

FIG. 3 is a flow diagram of an exemplary method for updating genericfile-classification definitions.

FIG. 4 is an illustration of an exemplary data cluster.

FIG. 5 is a block diagram of an exemplary computing system capable ofimplementing one or more of the embodiments described and/or illustratedherein.

FIG. 6 is a block diagram of an exemplary computing network capable ofimplementing one or more of the embodiments described and/or illustratedherein.

Throughout the drawings, identical reference characters and descriptionsindicate similar, but not necessarily identical, elements. While theexemplary embodiments described herein are susceptible to variousmodifications and alternative forms, specific embodiments have beenshown by way of example in the drawings and will be described in detailherein. However, the exemplary embodiments described herein are notintended to be limited to the particular forms disclosed. Rather, theinstant disclosure covers all modifications, equivalents, andalternatives falling within the scope of the appended claims.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The present disclosure is generally directed to systems and methods forupdating generic file-classification definitions. As will be explainedin greater detail below, upon classifying a data sample based at leastin part on a generic file-classification definition, the various systemsand methods described herein may query at least one verification serverin an attempt to verify the correctness of the classification of thedata sample. By querying the verification server, the various systemsand methods described herein may determine that the classification ofthe data sample is incorrect. In response to this determination, thevarious systems and methods described herein may modify the genericfile-classification definition to account for the incorrectclassification of the data sample.

Moreover, by modifying the generic file-classification definition at aclient device that encountered the data sample, the various systems andmethods described herein may conserve time and/or resources byeliminating the need to fully retrain the generic file-classificationdefinition. Additionally or alternatively, by modifying the genericfile-classification definition at the client device that encountered thedata sample, the various systems and methods described herein maycustomize the generic file-classification definition to the clientdevice based at least in part on the browsing behavior and/or patternsof the user of the client device.

The following will provide, with reference to FIGS. 1-2, detaileddescriptions of exemplary systems for updating genericfile-classification definitions. Detailed descriptions of correspondingcomputer-implemented methods will also be provided in connection withFIG. 3. Detailed descriptions of an exemplary data cluster will beprovided in connection with FIG. 4. In addition, detailed descriptionsof an exemplary computing system and network architecture capable ofimplementing one or more of the embodiments described herein will beprovided in connection with FIGS. 5 and 6, respectively.

FIG. 1 is a block diagram of an exemplary system 100 for updatinggeneric file-classification definitions. As illustrated in this figure,exemplary system 100 may include one or more modules 102 for performingone or more tasks. For example, and as will be explained in greaterdetail below, exemplary system 100 may include an identification module104 that identifies at least one generic file-classification definitiondeployed in a software product installed on a client device. Exemplarysystem 100 may also include a classification module 106 that classifiesat least one data sample encountered by the client device based at leastin part on the generic file-classification definition 140.

In addition, and as will be described in greater detail below, exemplarysystem 100 may include a query module 108 that queries at least oneverification server in an attempt to verify the correctness of theclassification of the data sample. Exemplary system 100 may also includea determination module 110 that determines that the classification ofthe data sample is incorrect based at least in part on the query.Exemplary system 100 may further include a modification module 112 thatmodifies the generic file-classification definition deployed in thesoftware product based at least in part on the data sample. Althoughillustrated as separate elements, one or more of modules 102 in FIG. 1may represent portions of a single module or application (such asSYMANTEC'S NORTON ANTIVIRUS, SYMANTEC'S NETWORK SECURITY, SYMANTEC'SNORTON INTERNET SECURITY, MCAFEE ALL ACCESS, MCAFEE TOTAL PROTECTION,MCAFEE INTERNET SECURITY, F-SECURE ANTI-VIRUS, TITANIUMANTIVIRUS+SECURITY, and/or KASPERSKY ANTI-VIRUS).

In certain embodiments, one or more of modules 102 in FIG. 1 mayrepresent one or more software applications or programs that, whenexecuted by a computing device, may cause the computing device toperform one or more tasks. For example, and as will be described ingreater detail below, one or more of modules 102 may represent softwaremodules stored and configured to run on one or more computing devices,such as the devices illustrated in FIG. 2 (e.g., client device 202and/or verification server 206), computing system 510 in FIG. 5, and/orportions of exemplary network architecture 600 in FIG. 6. One or more ofmodules 102 in FIG. 1 may also represent all or portions of one or morespecial-purpose computers configured to perform one or more tasks.

As illustrated in FIG. 1, exemplary system 100 may also include one ormore data samples, such as data sample 120. The phrase “data sample,” asused herein, generally refers to any type or form of computer data,metadata, features, characteristics, attributes, behaviors, and/orinformation related to a file. In one embodiment, data sample 120 mayrepresent a portion of data included in a file. Additionally oralternatively, data sample 120 may represent the entire file (includingall of the data, metadata, and/or information related to the file).Examples of data sample 120 include, without limitation, executablefiles, document files, data files, batch files, archive files, mediafiles, backup files, library files, compressed files, scripts, binarycode, machine code, portions of one or more of the same, combinations ofone or more of the same, or any other suitable data sample.

Examples of features, characteristics, and/or attributes of data sample120 include, without limitation, the name of data sample 120, the sizeof data sample 120, the storage location of data sample 120, the sourcecomputing device that hosts data sample 120, the file extension of datasample 120, the file format of data sample 120, the creation date and/ortime of data sample 120, the number of functions imported by data sample120, static features of data sample 120, dynamic features of data sample120, run-time behaviors of data sample 120, whether data sample 120generates visible windows for display, whether data sample 120 generatesnetwork traffic, combinations of one or more of the same, or any othersuitable metadata.

As illustrated in FIG. 1, exemplary system 100 may also include one ormore software products, such as software product 130. The phrase“software product,” as used herein, generally refers to any type or formof computer software and/or application. In one embodiment, softwareproduct 130 may include one or more generic file-classificationdefinitions used to classify data samples. Examples of software product130 include, without limitation, security software products,classification software products, client-side agents, SYMANTEC'S NORTONANTIVIRUS, SYMANTEC'S NETWORK SECURITY, SYMANTEC'S NORTON INTERNETSECURITY, MCAFEE ALL ACCESS, MCAFEE TOTAL PROTECTION, MCAFEE INTERNETSECURITY, F-SECURE ANTI-VIRUS, TITANIUM ANTIVIRUS+SECURITY, and/orKASPERSKY ANTI-VIRUS, portions of one or more of the same, combinationsof one or more of the same, or any other suitable software product.

As illustrated in FIG. 1, exemplary system 100 may also include one ormore generic file-classification definitions, such as genericfile-classification definition 140. The phrase “genericfile-classification definition,” as used herein, generally refers to anytype or form of tool and/or model capable of classifying data samples.In one embodiment, generic file-classification definition 140 mayinclude a plurality of features used to classify data samples.Additionally or alternatively, generic file-classification definition140 may facilitate classifying data samples by comparing the pluralityof features with the data samples. Examples of genericfile-classification definition 140 include, without limitation,signatures, heuristics, classifiers, data clusters, perceptrons,decision trees, combinations of one or of the same, or any othersuitable generic file-classification definition.

Examples of features used to classify data samples include, withoutlimitation, the name of data sample, the size of the data sample, thestorage location of the data sample, the source computing device thathosts the data sample, the file extension of the data sample, the fileformat of the data sample, the creation date and/or time of the datasample, the number of functions imported by the data sample, staticfeatures of the data sample, dynamic features of the data sample,run-time behaviors of the data sample, whether the data sample generatesvisible windows for display, whether the data sample generates networktraffic, combinations of one or more of the same, or any other suitablefeatures of data samples.

Exemplary system 100 in FIG. 1 may be implemented in a variety of ways.For example, all or a portion of exemplary system 100 may representportions of exemplary system 200 in FIG. 2. As shown in FIG. 2, system200 may include a client device 202 in communication with a verificationserver 206 via a network 204.

In one embodiment, client device 202 may be programmed with one or moreof modules 102. Although illustrated as external modules, modules 102may represent portions of software product 130. In this embodiment,client device 202 may include data sample 120 and/or software product130. Additionally or alternatively, software product 130 may include oneor more of generic file-classification definitions 140(1)-(N).

In one embodiment, verification server 206 may be programmed with one ormore of modules 102. Additionally or alternatively, verification server206 may include information 208 about data sample 120. As will bedescribed in greater detail below, information 208 may indicate thatdata sample 120 is either clean or malicious.

In one embodiment, one or more of modules 102 from FIG. 1 may, whenexecuted by at least one processor of client device 202 and/orverification server 206, enable client device 202 and/or verificationserver 206 to update generic file-classification definitions. Forexample, and as will be described in greater detail below, one or moreof modules 102 may cause client device 202 and/or verification server206 to (1) identify generic file-classification definition 140(1)deployed in software product 130 installed on client device 202, (2)classify data sample 120 encountered by client device 202 based at leastin part on generic file-classification definition 140(1), (3) queryverification server 206 in an attempt to verify the correctness of theclassification of data sample 120, (4) determine that the classificationof data sample 120 is incorrect based at least in part on the query, andthen (5) modify generic file-classification definition 140(1) deployedin software product 130 based at least in part on data sample 120.

Client device 202 generally represents any type or form of computingdevice capable of reading computer-executable instructions. Examples ofclient device 202 include, without limitation, laptops, tablets,desktops, servers, cellular phones, Personal Digital Assistants (PDAs),multimedia players, embedded systems, wearable devices (e.g., smartwatches, smart glasses, etc.), gaming consoles, combinations of one ormore of the same, exemplary computing system 510 in FIG. 5, or any othersuitable computing device.

Verification Server 206 generally represents any type or form ofcomputing device capable of verifying and/or contradictingclassifications of data samples. Examples of verification server 206include, without limitation, security servers, application servers, webservers, storage servers, and/or database servers configured to runcertain software applications and/or provide various security, web,storage, and/or database services.

Network 204 generally represents any medium or architecture capable offacilitating communication or data transfer. Examples of network 204include, without limitation, an intranet, a Wide Area Network (WAN), aLocal Area Network (LAN), a Personal Area Network (PAN), the Internet,Power Line Communications (PLC), a cellular network (e.g., a GlobalSystem for Mobile Communications (GSM) network), exemplary networkarchitecture 600 in FIG. 6, or the like. Network 204 may facilitatecommunication or data transfer using wireless or wired connections. Inone embodiment, network 204 may facilitate communication between clientdevice 202 and verification server 206.

FIG. 3 is a flow diagram of an exemplary computer-implemented method 300for updating generic file-classification definitions. The steps shown inFIG. 3 may be performed by any suitable computer-executable code and/orcomputing system. In some embodiments, the steps shown in FIG. 3 may beperformed by one or more of the components of system 100 in FIG. 1,system 200 in FIG. 2, computing system 510 in FIG. 5, and/or portions ofexemplary network architecture 600 in FIG. 6.

As illustrated in FIG. 3, at step 302 one or more of the systemsdescribed herein may identify at least one generic file-classificationdefinition deployed in a software product installed on a client device.For example, identification module 104 may, as part of client device 202and/or verification server 206 in FIG. 2, identify genericfile-classification definition 140(1) deployed in software product 130installed on client device 202. In this example, genericfile-classification definition 140(1) may include a plurality offeatures used to classify data samples encountered by client device 202.Additionally or alternatively, generic file-classification definition140(1) may facilitate classifying data samples by comparing theplurality of features with the data samples encountered by client device202.

The systems described herein may perform step 302 in a variety of ways.In some examples, identification module 104 may identify genericfile-classification definition 140(1) upon deployment of genericfile-classification definition 140(1). For example, a security server(not illustrated in FIG. 2) may generate generic file-classificationdefinition 140(1) for deployment in software product 130. The securityserver may then release generic file-classification 140(1) bydistributing and/or pushing generic file-classification definition140(1) to client device 202 via network 204.

Upon receiving generic file-classification definition 140(1) from thesecurity server, client device 202 may incorporate genericfile-classification definition 140(1) into software product 130. Asclient device 202 incorporates generic file-classification definition140(1) into software product 130, identification module 104 may identifygeneric file-classification definition 140(1).

In some examples, identification module 104 may identify genericfile-classification definition 140(1) upon installation of softwareproduct 130. For example, a user may install software product 130 onclient device 202. In this example, software product 130 may come withgeneric file-classification definition 140(1). During the installationof software product 130, identification module 104 may identify genericfile-classification definition 140(1) as a native component of softwareproduct 130.

In some examples, identification module 104 may identify certainfeatures of generic file-classification definition 140(1). For example,identification module 104 may identify a data cluster 400 in FIG. 4 inconnection with generic file-classification definition 140(1). Asillustrated in FIG. 4, data cluster 400 may include a centroid 420, adistance threshold 430, and/or training data samples 440(1)-(8).

The term “centroid,” as used herein, generally refers to any type orform of reference data point within a data cluster. The term “distancethreshold,” as used herein, generally refers to any type or form ofvalue, measurement, and/or metric that represents a certain distancefrom a centroid of a data cluster. In one embodiment, distance threshold430 may vary throughout data cluster 400. As illustrated in FIG. 4,distance threshold 430 may create a non-uniform (e.g., oval-shaped)virtual perimeter, as opposed to a uniform (e.g., circular) virtualperimeter, around centroid 420.

The phrase “training data sample,” as used herein, generally refers toany type or form of computer data, metadata, and/or information relatedto a known file. In one embodiment, training data samples 440(1)-(4) mayrepresent a set of known clean and/or non-malicious files. Additionallyor alternatively, training data samples 440(5)-(8) may represent a setof known malicious files.

In some examples, the security server may train genericfile-classification definition 140(1) based at least in part on trainingdata samples 440(1)-(8). Examples of training data samples 440(1)-(8)include, without limitation, executable files, document files, datafiles, batch files, archive files, media files, backup files, libraryfiles, compressed files, scripts, binary code, machine code, portions ofone or more of the same, combinations of one or more of the same, or anyother suitable training data samples.

As part of training generic file-classification definition 140(1), thesecurity server may apply at least one statistical algorithm to fitgeneric file-classification definition 140(1) to training data samples440(1)-(8). Examples of such a statistical algorithm include, withoutlimitation, Lloyd's algorithm, Voronoi interaction, linear regression,the perceptron algorithm, neural networking, regression trees,combinations of one or more of the same, or any other suitablestatistical algorithms.

In one example, the security server may calculate centroid 420 based atleast in part on the statistical algorithm. For example, the securityserver may apply the statistical algorithm to training data samples440(1)-(8). Upon applying the statistical algorithm, the security servermay calculate a value that represents the approximate center of trainingdata samples 440(1)-(8). The security server may then assign this valuethat represents the approximate center of training data samples440(1)-(8) to centroid 420.

Additionally or alternatively, the security server may determinedistance threshold 430 based at least in part on the statisticalalgorithm. For example, the security server may apply the statisticalalgorithm to training data samples 440(1)-(8). Upon applying thestatistical algorithm, the security server may calculate a value thatrepresents an approximate distance from centroid 420 that includestraining all of data samples 440(1)-(4) but excludes all of trainingdata samples 440(5)-(8). The security server may then assign this valuethat represents the approximate distance from centroid 420 to distancethreshold 430.

Returning to FIG. 3, at step 304 one or more of the systems describedherein may classify at least one data sample encountered by the clientdevice based at least in part on the generic file-classificationdefinition. For example, classification module 106 may, as part ofclient device 202 and/or verification server 206 in FIG. 2, classifydata sample 120 encountered by client device 202 based at least in parton generic file-classification definition 140(1). In this example, theclassification may identify data sample 120 as malicious. Additionallyor alternatively, the classification may identify data sample 120 asclean and/or non-malicious.

The systems described herein may perform step 304 in a variety of ways.In some examples, classification module 106 may classify data sample 120based at least in part on the distance from centroid 420 of data cluster400 in FIG. 4 to data sample 120. For example, client device 202 mayencounter data sample 120 while the user of client device 202 browsesthe Internet. As client device 202 encounters data sample 120,classification module 106 may analyze data sample 120 based at least inpart on generic file-classification definition 140(1).

As part of analyzing data sample 120, classification module 106 mayapply generic file-classification definition 140(1) to data sample 120.For example, classification module 106 may identify certain features,characteristics, and/or attributes of data sample 120. In this example,classification module 106 may compare these features, characteristics,and/or attributes of data sample 120 with generic file-classificationdefinition 140(1). Classification module 106 may then compute thedistance from centroid 420 of data cluster 400 in FIG. 4 to data sample120 based at least in part on this comparison.

Upon computing the distance from centroid 420 to data sample 120,classification module 106 may classify data sample 120 based at least inpart on the distance from centroid 420 to data sample 120. For example,classification module 106 may apply a distance function to data sample120. In this example, the distance function may generate a value thatrepresents the distance from centroid 420 to data sample 120.Classification module 106 may determine that the value representing thedistance from centroid 420 to data sample 120 is below distancethreshold 430 in FIG. 4 by comparing this value with distance threshold430. Classification module 106 may then classify data sample 120 basedat least in part on this determination.

Additionally or alternatively, classification module 106 may determinethat the value representing the distance from centroid 420 to datasample 120 is above distance threshold 430 in FIG. 4 by comparing thisvalue with distance threshold 430. Classification module 106 may thenclassify data sample 120 based at least in part on this determination.

In one embodiment, distance threshold 430 in FIG. 4 may represent adistance below which data samples are likely to include malware. Asillustrated in FIG. 4, data sample 120 may fall inside of the virtualperimeter created by distance threshold 430. In other words, data sample120 may lie between centroid 420 and distance threshold 430. In thisembodiment, classification module 106 may classify data sample 120 asmalware due at least in part to the distance from centroid 420 to datasample 120 being below distance threshold 430.

In some examples, classification module 106 may classify at least oneadditional data samples encountered by client device 202. For example,client device 202 may encounter data sample 450 in FIG. 4 while the userof client device 202 browses the Internet. As client device 202encounters data sample 450, classification module 106 may analyze datasample 450 based at least in part on generic file-classificationdefinition 140(1).

As part of analyzing data sample 450, classification module 106 mayapply generic file-classification definition 140(1) to data sample 450.For example, classification module 106 may identify certain features,characteristics, and/or attributes of data sample 450. In this example,classification module 106 may compare these features, characteristics,and/or attributes of data sample 450 with generic file-classificationdefinition 140(1). Classification module 106 may then compute thedistance from centroid 420 of data cluster 400 in FIG. 4 to data sample120 based at least in part on this comparison. Upon computing thedistance from centroid 420 to data sample 450, classification module 106may classify data sample 450 based at least in part on the distance fromcentroid 420 to data sample 450.

In one embodiment, distance threshold 430 in FIG. 4 may represent adistance above which data samples are unlikely to include malware. Asillustrated in FIG. 4, data sample 450 may fall outside of the virtualperimeter created by distance threshold 430. In other words, data sample450 may lie beyond distance threshold 430 relative to centroid 420. Inthis embodiment, classification module 106 may classify data sample 450as non-malware due at least in part to the distance from centroid 420 todata sample 450 being above distance threshold 430.

Returning to FIG. 3, at step 306 one or more of the systems describedherein may query at least one verification server 206 in an attempt toverify the correctness of the classification of the data sample. Forexample, query module 108 may, as part of client device 202 and/orverification server 206 in FIG. 2, query verification server 206 in anattempt to verify the correctness of the classification of data sample120. By querying verification server 206, query module 108 may determinewhether verification server 206 is aware of any information about datasample 120 that verifies or contradicts the classification.

The systems described herein may perform step 306 in a variety of ways.In some examples, query module 108 may query verification server 206 asto whether the classification of data sample 120 is correct. Forexample, query module 108 may direct client device 202 to notifyverification server 206 that classification module 106 has classifieddata sample 120 as malware. Query module 108 may also direct clientdevice 202 to query verification server 206 as to whether thisclassification of data sample 120 as malware is correct.

Additionally or alternatively, query module 108 may direct client device202 to notify verification server 206 that classification module 106 hasclassified data sample 450 as non-malware. Query module 108 may alsodirect client device 202 to query verification server 206 as to whetherthis classification of data sample 450 as non-malware is correct.

In some examples, query module 108 may request any information aboutdata sample 120 that was unavailable when generic file-classificationdefinition 140(1) was released. For example, query module 108 may directclient device 202 to notify verification server 206 that client device202 has encountered data sample 120. Additionally or alternatively,query module 108 may direct client device 202 to request any informationabout data sample 120 that became available after the release of genericfile-classification definition 140(1).

Returning to FIG. 3, at step 308 one or more of the systems describedherein may determine that the classification of the data sample isincorrect based at least in part on the query. For example,determination module 110 may, as part of client device 202 and/orverification server 206 in FIG. 2, determine that the classification ofdata sample 120 is incorrect based at least in part on the query.

The systems described herein may perform step 308 in a variety of ways.In some examples, determination module 110 may determine that theclassification of data sample 120 is incorrect based at least in part ona response from verification server 206. For example, verificationserver 206 may provide client device 202 with a response to the query.As client device 202 receives the response from verification server 206,determination module 110 may determine that the response contradicts theclassification of data sample 120. Determination module 110 may thendetermine that the classification of data sample 120 is incorrect basedat least in part on this response.

In one embodiment, the response may indicate that data sample 120 is notmalware despite the classification of data sample 120 as malware.Additionally or alternatively, the response may indicate that datasample 120 is malware despite the classification of data sample 120 asnon-malware.

In some examples, determination module 110 may obtain information 208about data sample 120 that was unavailable when genericfile-classification definition 140(1) was released. For example,verification server 206 may provide client device 202 with information208 about data sample 120 in response to the request. As client device202 receives information 208 from verification server 206, determinationmodule 110 may determine that information 208 contradicts theclassification of data sample 120. Determination module 110 may thendetermine that the classification of data sample 120 is incorrect basedat least in part on information 208.

Returning to FIG. 3, at step 310 one or more of the systems describedherein may modify the generic file-classification definition deployed inthe software product to account for the incorrect classification basedat least in part on the data sample. For example, modification module112 may, as part of client device 202 and/or verification server 206 inFIG. 2, modify generic file-classification definition 140(1) deployed insoftware product 130 based at least in part on data sample 120. In thisexample, the modification to generic file-classification definition140(1) may account for and/or address the incorrect classification ofdata sample 120.

The systems described herein may perform step 310 in a variety of ways.In some examples, modification module 112 may modify genericfile-classification definition 140(1) by decreasing distance threshold430. For example, as described above, classification module 106 mayclassify data sample 120 as malware due at least in part to the distancefrom centroid 420 to data sample 120 being below distance threshold 430.In this example, determination module 110 may determine that thisclassification of data sample 120 as malware is incorrect based at leastin part on the query.

In response to this determination, modification module 112 may modifygeneric file-classification definition 140(1) by decreasing distancethreshold 430 such that the distance from centroid 420 to data sample120 is above distance threshold 430. For example, modification module112 may decrease distance threshold 430 within data sample 120 such thatgeneric file-classification definition 140(1) classifies data sample 120as malware. In other words, this decrease to distance threshold 430 maycause generic file-classification 140(1) to classify data sample 120 asnon-malware if generic file-classification 140(1) were to classify datasample 120 anew.

In some examples, modification module 112 may modify genericfile-classification definition 140(1) by increasing distance threshold430. For example, as described above, classification module 106 mayclassify data sample 450 as non-malware due at least in part to thedistance from centroid 420 to data sample 450 being above distancethreshold 430. In this example, determination module 110 may determinethat this classification of data sample 450 as non-malware is incorrectbased at least in part on the query.

In response to this determination, modification module 112 may modifygeneric file-classification definition 140(1) by increasing distancethreshold 430 such that the distance from centroid 420 to data sample450 is below distance threshold 430. For example, modification module112 may increase distance threshold 430 beyond data sample 450 such thatgeneric file-classification definition 140(1) classifies data sample 120as malware. In other words, this increase to distance threshold 430 maycause generic file-classification 140(1) to classify data sample 450 asmalware if generic file-classification 140(1) were to classify datasample 450 anew.

In some examples, modification module 112 may customize genericfile-classification definition 140(1) to client device 202 based atleast in part on the browsing behavior and/or patterns of the user ofclient device 202. For example, as described above, classificationmodule 106 may classify data samples 120 and 450 encountered by clientdevice 202 based at least in part on generic file-classificationdefinition 140(1). In this example, determination module 110 maydetermine that the classifications of data samples 120 and 450 areincorrect. Additionally or alternatively, modification module 112 maycustomize generic file-classification definition 140(1) to client device202 based at least in part on the incorrect classifications of datasamples 120 and 450 encountered by client device 202.

As explained above in connection with exemplary method 300 in FIG. 3, asecurity software product may update generic file-classificationdefinitions to account for newly identified clean and/or maliciousfiles. For example, a security software vendor may generate a genericfile-classification definition from a set of training data that includesknown clean and/or malicious files. However, after generating thegeneric file-classification definition and releasing the same to asecurity software product installed on a client device, the securitysoftware vendor may identify new clean and/or malicious files. Since theset of training data did not include these newly identified files, thegeneric file-classification definition may fail to account for certaininformation derived from these newly identified files. As a result, thegeneric file-classification definition may cause the security softwareproduct to produce a false negative and/or false positive uponencountering one of these files on the end-user's computing device.

In an effort to address this deficiency, the security software productinstalled on the client device may query at least one verificationserver in an attempt to verify the correctness of a classification of adata sample by the generic file-classification definition. By queryingthe verification server, the security software product may determinethat the classification of the data sample is incorrect. In response tothis determination, the security software product may modify the genericfile-classification definition to account for the incorrectclassification of the data sample.

Moreover, by modifying the generic file-classification definition at theclient device, the security software product may conserve time and/orresources by eliminating the need to fully retrain the genericfile-classification definition. Additionally or alternatively, bymodifying the generic file-classification definition at the clientdevice, the security software product may customize the genericfile-classification definition to the client device based at least inpart on the browsing behavior and/or patterns of the user of the clientdevice.

FIG. 5 is a block diagram of an exemplary computing system 510 capableof implementing one or more of the embodiments described and/orillustrated herein. For example, all or a portion of computing system510 may perform and/or be a means for performing, either alone or incombination with other elements, one or more of the steps describedherein (such as one or more of the steps illustrated in FIG. 3). All ora portion of computing system 510 may also perform and/or be a means forperforming any other steps, methods, or processes described and/orillustrated herein.

Computing system 510 broadly represents any single or multi-processorcomputing device or system capable of executing computer-readableinstructions. Examples of computing system 510 include, withoutlimitation, workstations, laptops, client-side terminals, servers,distributed computing systems, handheld devices, or any other computingsystem or device. In its most basic configuration, computing system 510may include at least one processor 514 and a system memory 516.

Processor 514 generally represents any type or form of physicalprocessing unit (e.g., a hardware-implemented central processing unit)capable of processing data or interpreting and executing instructions.In certain embodiments, processor 514 may receive instructions from asoftware application or module. These instructions may cause processor514 to perform the functions of one or more of the exemplary embodimentsdescribed and/or illustrated herein.

System memory 516 generally represents any type or form of volatile ornon-volatile storage device or medium capable of storing data and/orother computer-readable instructions. Examples of system memory 516include, without limitation, Random Access Memory (RAM), Read OnlyMemory (ROM), flash memory, or any other suitable memory device.Although not required, in certain embodiments computing system 510 mayinclude both a volatile memory unit (such as, for example, system memory516) and a non-volatile storage device (such as, for example, primarystorage device 532, as described in detail below). In one example, oneor more of modules 102 from FIG. 1 may be loaded into system memory 516.

In certain embodiments, exemplary computing system 510 may also includeone or more components or elements in addition to processor 514 andsystem memory 516. For example, as illustrated in FIG. 5, computingsystem 510 may include a memory controller 518, an Input/Output (I/O)controller 520, and a communication interface 522, each of which may beinterconnected via a communication infrastructure 512. Communicationinfrastructure 512 generally represents any type or form ofinfrastructure capable of facilitating communication between one or morecomponents of a computing device. Examples of communicationinfrastructure 512 include, without limitation, a communication bus(such as an Industry Standard Architecture (ISA), Peripheral ComponentInterconnect (PCI), PCI Express (PCIe), or similar bus) and a network.

Memory controller 518 generally represents any type or form of devicecapable of handling memory or data or controlling communication betweenone or more components of computing system 510. For example, in certainembodiments memory controller 518 may control communication betweenprocessor 514, system memory 516, and I/O controller 520 viacommunication infrastructure 512.

I/O controller 520 generally represents any type or form of modulecapable of coordinating and/or controlling the input and outputfunctions of a computing device. For example, in certain embodiments I/Ocontroller 520 may control or facilitate transfer of data between one ormore elements of computing system 510, such as processor 514, systemmemory 516, communication interface 522, display adapter 526, inputinterface 530, and storage interface 534.

Communication interface 522 broadly represents any type or form ofcommunication device or adapter capable of facilitating communicationbetween exemplary computing system 510 and one or more additionaldevices. For example, in certain embodiments communication interface 522may facilitate communication between computing system 510 and a privateor public network including additional computing systems. Examples ofcommunication interface 522 include, without limitation, a wired networkinterface (such as a network interface card), a wireless networkinterface (such as a wireless network interface card), a modem, and anyother suitable interface. In at least one embodiment, communicationinterface 522 may provide a direct connection to a remote server via adirect link to a network, such as the Internet. Communication interface522 may also indirectly provide such a connection through, for example,a local area network (such as an Ethernet network), a personal areanetwork, a telephone or cable network, a cellular telephone connection,a satellite data connection, or any other suitable connection.

In certain embodiments, communication interface 522 may also represent ahost adapter configured to facilitate communication between computingsystem 510 and one or more additional network or storage devices via anexternal bus or communications channel. Examples of host adaptersinclude, without limitation, Small Computer System Interface (SCSI) hostadapters, Universal Serial Bus (USB) host adapters, Institute ofElectrical and Electronics Engineers (IEEE) 1394 host adapters, AdvancedTechnology Attachment (ATA), Parallel ATA (PATA), Serial ATA (SATA), andExternal SATA (eSATA) host adapters, Fibre Channel interface adapters,Ethernet adapters, or the like. Communication interface 522 may alsoallow computing system 510 to engage in distributed or remote computing.For example, communication interface 522 may receive instructions from aremote device or send instructions to a remote device for execution.

As illustrated in FIG. 5, computing system 510 may also include at leastone display device 524 coupled to communication infrastructure 512 via adisplay adapter 526. Display device 524 generally represents any type orform of device capable of visually displaying information forwarded bydisplay adapter 526. Similarly, display adapter 526 generally representsany type or form of device configured to forward graphics, text, andother data from communication infrastructure 512 (or from a framebuffer, as known in the art) for display on display device 524.

As illustrated in FIG. 5, exemplary computing system 510 may alsoinclude at least one input device 528 coupled to communicationinfrastructure 512 via an input interface 530. Input device 528generally represents any type or form of input device capable ofproviding input, either computer or human generated, to exemplarycomputing system 510. Examples of input device 528 include, withoutlimitation, a keyboard, a pointing device, a speech recognition device,or any other input device.

As illustrated in FIG. 5, exemplary computing system 510 may alsoinclude a primary storage device 532 and a backup storage device 533coupled to communication infrastructure 512 via a storage interface 534.Storage devices 532 and 533 generally represent any type or form ofstorage device or medium capable of storing data and/or othercomputer-readable instructions. For example, storage devices 532 and 533may be a magnetic disk drive (e.g., a so-called hard drive), a solidstate drive, a floppy disk drive, a magnetic tape drive, an optical diskdrive, a flash drive, or the like. Storage interface 534 generallyrepresents any type or form of interface or device for transferring databetween storage devices 532 and 533 and other components of computingsystem 510.

In certain embodiments, storage devices 532 and 533 may be configured toread from and/or write to a removable storage unit configured to storecomputer software, data, or other computer-readable information.Examples of suitable removable storage units include, withoutlimitation, a floppy disk, a magnetic tape, an optical disk, a flashmemory device, or the like. Storage devices 532 and 533 may also includeother similar structures or devices for allowing computer software,data, or other computer-readable instructions to be loaded intocomputing system 510. For example, storage devices 532 and 533 may beconfigured to read and write software, data, or other computer-readableinformation. Storage devices 532 and 533 may also be a part of computingsystem 510 or may be a separate device accessed through other interfacesystems.

Many other devices or subsystems may be connected to computing system510. Conversely, all of the components and devices illustrated in FIG. 5need not be present to practice the embodiments described and/orillustrated herein. The devices and subsystems referenced above may alsobe interconnected in different ways from that shown in FIG. 5. Computingsystem 510 may also employ any number of software, firmware, and/orhardware configurations. For example, one or more of the exemplaryembodiments disclosed herein may be encoded as a computer program (alsoreferred to as computer software, software applications,computer-readable instructions, or computer control logic) on acomputer-readable medium. The phrase “computer-readable medium,” as usedherein, generally refers to any form of device, carrier, or mediumcapable of storing or carrying computer-readable instructions. Examplesof computer-readable media include, without limitation,transmission-type media, such as carrier waves, and non-transitory-typemedia, such as magnetic-storage media (e.g., hard disk drives, tapedrives, and floppy disks), optical-storage media (e.g., Compact Disks(CDs), Digital Video Disks (DVDs), and BLU-RAY disks),electronic-storage media (e.g., solid-state drives and flash media), andother distribution systems.

The computer-readable medium containing the computer program may beloaded into computing system 510. All or a portion of the computerprogram stored on the computer-readable medium may then be stored insystem memory 516 and/or various portions of storage devices 532 and533. When executed by processor 514, a computer program loaded intocomputing system 510 may cause processor 514 to perform and/or be ameans for performing the functions of one or more of the exemplaryembodiments described and/or illustrated herein. Additionally oralternatively, one or more of the exemplary embodiments described and/orillustrated herein may be implemented in firmware and/or hardware. Forexample, computing system 510 may be configured as an ApplicationSpecific Integrated Circuit (ASIC) adapted to implement one or more ofthe exemplary embodiments disclosed herein.

FIG. 6 is a block diagram of an exemplary network architecture 600 inwhich client systems 610, 620, and 630 and servers 640 and 645 may becoupled to a network 650. As detailed above, all or a portion of networkarchitecture 600 may perform and/or be a means for performing, eitheralone or in combination with other elements, one or more of the stepsdisclosed herein (such as one or more of the steps illustrated in FIG.3). All or a portion of network architecture 600 may also be used toperform and/or be a means for performing other steps and features setforth in the instant disclosure.

Client systems 610, 620, and 630 generally represent any type or form ofcomputing device or system, such as exemplary computing system 510 inFIG. 5. Similarly, servers 640 and 645 generally represent computingdevices or systems, such as application servers or database servers,configured to provide various database services and/or run certainsoftware applications. Network 650 generally represents anytelecommunication or computer network including, for example, anintranet, a WAN, a LAN, a PAN, or the Internet. In one example, clientsystems 610, 620, and/or 630 and/or servers 640 and/or 645 may includeall or a portion of system 100 from FIG. 1.

As illustrated in FIG. 6, one or more storage devices 660(1)-(N) may bedirectly attached to server 640. Similarly, one or more storage devices670(1)-(N) may be directly attached to server 645. Storage devices660(1)-(N) and storage devices 670(1)-(N) generally represent any typeor form of storage device or medium capable of storing data and/or othercomputer-readable instructions. In certain embodiments, storage devices660(1)-(N) and storage devices 670(1)-(N) may represent Network-AttachedStorage (NAS) devices configured to communicate with servers 640 and 645using various protocols, such as Network File System (NFS), ServerMessage Block (SMB), or Common Internet File System (CIFS).

Servers 640 and 645 may also be connected to a Storage Area Network(SAN) fabric 680. SAN fabric 680 generally represents any type or formof computer network or architecture capable of facilitatingcommunication between a plurality of storage devices. SAN fabric 680 mayfacilitate communication between servers 640 and 645 and a plurality ofstorage devices 690(1)-(N) and/or an intelligent storage array 695. SANfabric 680 may also facilitate, via network 650 and servers 640 and 645,communication between client systems 610, 620, and 630 and storagedevices 690(1)-(N) and/or intelligent storage array 695 in such a mannerthat devices 690(1)-(N) and array 695 appear as locally attached devicesto client systems 610, 620, and 630. As with storage devices 660(1)-(N)and storage devices 670(1)-(N), storage devices 690(1)-(N) andintelligent storage array 695 generally represent any type or form ofstorage device or medium capable of storing data and/or othercomputer-readable instructions.

In certain embodiments, and with reference to exemplary computing system510 of FIG. 5, a communication interface, such as communicationinterface 522 in FIG. 5, may be used to provide connectivity betweeneach client system 610, 620, and 630 and network 650. Client systems610, 620, and 630 may be able to access information on server 640 or 645using, for example, a web browser or other client software. Suchsoftware may allow client systems 610, 620, and 630 to access datahosted by server 640, server 645, storage devices 660(1)-(N), storagedevices 670(1)-(N), storage devices 690(1)-(N), or intelligent storagearray 695. Although FIG. 6 depicts the use of a network (such as theInternet) for exchanging data, the embodiments described and/orillustrated herein are not limited to the Internet or any particularnetwork-based environment.

In at least one embodiment, all or a portion of one or more of theexemplary embodiments disclosed herein may be encoded as a computerprogram and loaded onto and executed by server 640, server 645, storagedevices 660(1)-(N), storage devices 670(1)-(N), storage devices690(1)-(N), intelligent storage array 695, or any combination thereof.All or a portion of one or more of the exemplary embodiments disclosedherein may also be encoded as a computer program, stored in server 640,run by server 645, and distributed to client systems 610, 620, and 630over network 650.

As detailed above, computing system 510 and/or one or more components ofnetwork architecture 600 may perform and/or be a means for performing,either alone or in combination with other elements, one or more steps ofan exemplary method for updating generic file-classificationdefinitions.

While the foregoing disclosure sets forth various embodiments usingspecific block diagrams, flowcharts, and examples, each block diagramcomponent, flowchart step, operation, and/or component described and/orillustrated herein may be implemented, individually and/or collectively,using a wide range of hardware, software, or firmware (or anycombination thereof) configurations. In addition, any disclosure ofcomponents contained within other components should be consideredexemplary in nature since many other architectures can be implemented toachieve the same functionality.

In some examples, all or a portion of exemplary system 100 in FIG. 1 mayrepresent portions of a cloud-computing or network-based environment.Cloud-computing environments may provide various services andapplications via the Internet. These cloud-based services (e.g.,software as a service, platform as a service, infrastructure as aservice, etc.) may be accessible through a web browser or other remoteinterface. Various functions described herein may be provided through aremote desktop environment or any other cloud-based computingenvironment.

In various embodiments, all or a portion of exemplary system 100 in FIG.1 may facilitate multi-tenancy within a cloud-based computingenvironment. In other words, the software modules described herein mayconfigure a computing system (e.g., a server) to facilitatemulti-tenancy for one or more of the functions described herein. Forexample, one or more of the software modules described herein mayprogram a server to enable two or more clients (e.g., customers) toshare an application that is running on the server. A server programmedin this manner may share an application, operating system, processingsystem, and/or storage system among multiple customers (i.e., tenants).One or more of the modules described herein may also partition dataand/or configuration information of a multi-tenant application for eachcustomer such that one customer cannot access data and/or configurationinformation of another customer.

According to various embodiments, all or a portion of exemplary system100 in FIG. 1 may be implemented within a virtual environment. Forexample, the modules and/or data described herein may reside and/orexecute within a virtual machine. As used herein, the phrase “virtualmachine” generally refers to any operating system environment that isabstracted from computing hardware by a virtual machine manager (e.g., ahypervisor). Additionally or alternatively, the modules and/or datadescribed herein may reside and/or execute within a virtualizationlayer. As used herein, the phrase “virtualization layer” generallyrefers to any data layer and/or application layer that overlays and/oris abstracted from an operating system environment. A virtualizationlayer may be managed by a software virtualization solution (e.g., a filesystem filter) that presents the virtualization layer as though it werepart of an underlying base operating system. For example, a softwarevirtualization solution may redirect calls that are initially directedto locations within a base file system and/or registry to locationswithin a virtualization layer.

In some examples, all or a portion of exemplary system 100 in FIG. 1 mayrepresent portions of a mobile computing environment. Mobile computingenvironments may be implemented by a wide range of mobile computingdevices, including mobile phones, tablet computers, e-book readers,personal digital assistants, wearable computing devices (e.g., computingdevices with a head-mounted display, smartwatches, etc.), and the like.In some examples, mobile computing environments may have one or moredistinct features, including, for example, reliance on battery power,presenting only one foreground application at any given time, remotemanagement features, touchscreen features, location and movement data(e.g., provided by Global Positioning Systems, gyroscopes,accelerometers, etc.), restricted platforms that restrict modificationsto system-level configurations and/or that limit the ability ofthird-party software to inspect the behavior of other applications,controls to restrict the installation of applications (e.g., to onlyoriginate from approved application stores), etc. Various functionsdescribed herein may be provided for a mobile computing environmentand/or may interact with a mobile computing environment.

In addition, all or a portion of exemplary system 100 in FIG. 1 mayrepresent portions of, interact with, consume data produced by, and/orproduce data consumed by one or more systems for information management.As used herein, the phrase “information management” may refer to theprotection, organization, and/or storage of data. Examples of systemsfor information management may include, without limitation, storagesystems, backup systems, archival systems, replication systems, highavailability systems, data search systems, virtualization systems, andthe like.

In some embodiments, all or a portion of exemplary system 100 in FIG. 1may represent portions of, produce data protected by, and/or communicatewith one or more systems for information security. As used herein, thephrase “information security” may refer to the control of access toprotected data. Examples of systems for information security mayinclude, without limitation, systems providing managed securityservices, data loss prevention systems, identity authentication systems,access control systems, encryption systems, policy compliance systems,intrusion detection and prevention systems, electronic discoverysystems, and the like.

According to some examples, all or a portion of exemplary system 100 inFIG. 1 may represent portions of, communicate with, and/or receiveprotection from one or more systems for endpoint security. As usedherein, the phrase “endpoint security” may refer to the protection ofendpoint systems from unauthorized and/or illegitimate use, access,and/or control. Examples of systems for endpoint protection may include,without limitation, anti-malware systems, user authentication systems,encryption systems, privacy systems, spam-filtering services, and thelike.

The process parameters and sequence of steps described and/orillustrated herein are given by way of example only and can be varied asdesired. For example, while the steps illustrated and/or describedherein may be shown or discussed in a particular order, these steps donot necessarily need to be performed in the order illustrated ordiscussed. The various exemplary methods described and/or illustratedherein may also omit one or more of the steps described or illustratedherein or include additional steps in addition to those disclosed.

While various embodiments have been described and/or illustrated hereinin the context of fully functional computing systems, one or more ofthese exemplary embodiments may be distributed as a program product in avariety of forms, regardless of the particular type of computer-readablemedia used to actually carry out the distribution. The embodimentsdisclosed herein may also be implemented using software modules thatperform certain tasks. These software modules may include script, batch,or other executable files that may be stored on a computer-readablestorage medium or in a computing system. In some embodiments, thesesoftware modules may configure a computing system to perform one or moreof the exemplary embodiments disclosed herein.

In addition, one or more of the modules described herein may transformdata, physical devices, and/or representations of physical devices fromone form to another. For example, one or more of the modules recitedherein may receive a generic file-classification definition to betransformed, transform the generic file-classification definition,output a result of the transformation to a computing device, use theresult of the transformation to classify data samples, and store theresult of the transformation for future use. Additionally oralternatively, one or more of the modules recited herein may transform aprocessor, volatile memory, non-volatile memory, and/or any otherportion of a physical computing device from one form to another byexecuting on the computing device, storing data on the computing device,and/or otherwise interacting with the computing device.

The preceding description has been provided to enable others skilled inthe art to best utilize various aspects of the exemplary embodimentsdisclosed herein. This exemplary description is not intended to beexhaustive or to be limited to any precise form disclosed. Manymodifications and variations are possible without departing from thespirit and scope of the instant disclosure. The embodiments disclosedherein should be considered in all respects illustrative and notrestrictive. Reference should be made to the appended claims and theirequivalents in determining the scope of the instant disclosure.

Unless otherwise noted, the terms “connected to” and “coupled to” (andtheir derivatives), as used in the specification and claims, are to beconstrued as permitting both direct and indirect (i.e., via otherelements or components) connection. In addition, the terms “a” or “an,”as used in the specification and claims, are to be construed as meaning“at least one of.” Finally, for ease of use, the terms “including” and“having” (and their derivatives), as used in the specification andclaims, are interchangeable with and have the same meaning as the word“comprising.”

What is claimed is:
 1. A computer-implemented method for updating generic file-classification definitions, at least a portion of the method being performed by a computing device comprising at least one processor, the method comprising: identifying at least one generic file-classification definition deployed in a software product installed on a client device, the generic file-classification definition comprising a data cluster that: has been trained by a set of known malicious files and a set of known clean files; and has been fit to the set of known malicious files and the set of known clean files by applying a statistical algorithm; wherein the data cluster comprises: a plurality of training data samples; a centroid that: has been calculated by applying the statistical algorithm to the set of known malicious files and the set of known clean files; and represents an approximate center of the set of known malicious files and the set of known clean files; a distance threshold that varies throughout the data cluster; and a non-uniform virtual perimeter created by the distance threshold that varies throughout the data cluster; computing a distance from the centroid of the data cluster to a data sample encountered by the client device; determining that the distance from the centroid to the data sample is below the distance threshold; in response to determining that the distance from the centroid to the data sample is below the distance threshold, classifying the data sample as malware based at least in part on the distance from the centroid to the data sample; querying at least one verification server in an attempt to verify the correctness of the malware classification of the data sample by obtaining information about the data sample that was unavailable when the generic file-classification definition was released, the information indicating that the data sample is clean even though the generic file-classification definition classified the data sample as malware; determining, based at least in part on the information about the data sample that was unavailable when the generic file-classification definition was released, that the malware classification of the data sample is incorrect; and in response to determining that the malware classification of the data sample is incorrect, modifying the generic file-classification definition deployed in the software product based at least in part on the data sample to account for the incorrect classification of the data sample without retraining the generic file-classification definition, wherein modifying the generic file-classification definition comprises: customizing the generic file-classification definition to the client device based at least in part on browsing behavior or patterns of a user of the client device and due at least in part to the client device having encountered the data sample that was incorrectly classified; and decreasing the distance threshold such that: the distance from the centroid to the data sample is above the distance threshold; and the generic file-classification definition would classify the data sample as clean in the event that the generic file-classification definition were to classify the data sample anew.
 2. The computer-implemented method of claim 1, wherein: computing the distance from the centroid to the data sample comprises applying, to the data sample, a distance function that generates a value representing the distance from the centroid to the data sample; and classifying the data sample comprises determining that the value representing the distance from the centroid to the data sample is below the distance threshold by comparing the value with the distance threshold.
 3. The computer-implemented method of claim 1, wherein: computing the distance from the centroid to the data sample comprises applying, to the data sample, a distance function that generates a value representing the distance from the centroid to the data sample; and classifying the data sample comprises determining that the value representing the distance from the centroid to the data sample is above the distance threshold by comparing the value with the distance threshold.
 4. The computer-implemented method of claim 1, wherein: classifying the data sample comprises determining that the distance from the centroid to the data sample is above the distance threshold; and modifying the generic file-classification definition comprises increasing the distance threshold such that the distance from the centroid to the data sample is below the distance threshold.
 5. The computer-implemented method of claim 4, wherein: the distance threshold represents a distance above which data samples are unlikely to include malware; classifying the data sample comprises classifying the data sample as non-malware due at least in part to the distance from the centroid to the data sample being above the distance threshold; and increasing the distance threshold comprises increasing the distance threshold beyond the data sample such that the generic file-classification definition classifies the data sample as malware.
 6. The computer-implemented method of claim 1, wherein: the distance threshold represents a distance below which data samples are likely to include malware; classifying the data sample comprises classifying the data sample as malware due at least in part to the distance from the centroid to the data sample being below the distance threshold; and decreasing the distance threshold comprises decreasing the distance threshold within the data sample such that the generic file-classification definition classifies the data sample as non-malware.
 7. The computer-implemented method of claim 1, wherein: the centroid comprises a reference data point calculated based at least in part on the plurality of training data samples; and the distance threshold comprises a reference distance determined based at least in part on the plurality of training data samples.
 8. The computer-implemented method of claim 1, wherein: classifying the data sample encountered by the client device comprises classifying, based at least in part on the generic file-classification definition, a plurality of data samples encountered by the client device; and modifying the generic file-classification definition comprises customizing, based at least in part on the plurality of data samples encountered by the client device, the generic file-classification definition to the client device.
 9. A system for updating generic file-classification definitions comprising: an identification module, stored in memory, that identifies at least one generic file-classification definition deployed in a software product installed on a client device, the generic file-classification definition comprising a data cluster that: has been trained by a set of known malicious files and a set of known clean files; and has been fit to the set of known malicious files and the set of known clean files by applying a statistical algorithm; wherein the data cluster comprises: a plurality of training data samples; a centroid that: has been calculated by applying the statistical algorithm to the set of known malicious files and the set of known clean files; and represents an approximate center of the set of known malicious files and the set of known clean files; a distance threshold that varies throughout the data cluster; and a non-uniform virtual perimeter created by the distance threshold that varies throughout the data cluster; a classification module, stored in memory, that: computes a distance from the centroid of the data cluster to a data sample encountered by the client device; determines that the distance from the centroid to the data sample is below the distance threshold; and classify, in response to determining that the distance from the centroid to the data sample is below the distance threshold, the data sample as malware based at least in part on the distance from the centroid to the data sample; a query module, stored in memory, that queries at least one verification server in an attempt to verify the correctness of the malware classification of the data sample by obtaining information about the data sample that was unavailable when the generic file-classification definition was released, the information indicating that the data sample is clean even though the generic file-classification definition classified the data sample as malware; a determination module, stored in memory, that determines, based at least in part on the information about the data sample that was unavailable when the generic file-classification definition was released, that the malware classification of the data sample is incorrect; a modification module, stored in memory, that modifies, in response to the determination that the malware classification of the data sample is incorrect, the generic file-classification definition deployed in the software product based at least in part on the data sample to account for the incorrect classification of the data sample without retraining the generic file-classification definition, wherein modifying the generic file-classification definition comprises: customizing the generic file-classification definition to the client device based at least in part on browsing behavior or patterns of a user of the client device and due at least in part to the client device having encountered the data sample that was incorrectly classified; and decreasing the distance threshold such that: the distance from the centroid to the data sample is above the distance threshold; and the generic file-classification definition would classify the data sample as clean in the event that the generic file-classification definition were to classify the data sample anew; and at least one physical processor configured to execute the identification module, the classification module, the query module, the determination module, and the modification module.
 10. The system of claim 9, wherein the classification module: applies, to the data sample, a distance function that generates a value representing the distance from the centroid to the data sample; and determines that the value representing the distance from the centroid to the data sample is below the distance threshold by comparing the value with the distance threshold.
 11. The system of claim 9, wherein the classification module: applies, to the data sample, a distance function that generates a value representing the distance from the centroid to the data sample; and determines that the value representing the distance from the centroid to the data sample is above the distance threshold by comparing the value representing the distance from the centroid to the data sample with the distance threshold.
 12. The system of claim 9, wherein: the classification module classifies the data sample by determining that the distance from the centroid to the data sample is above the distance threshold; and the modification module modifies the generic file-classification definition by increasing the distance threshold such that the distance from the centroid to the data sample is below the distance threshold.
 13. The system of claim 9, wherein: the centroid comprises a data point calculated based at least in part on the plurality of training data samples; and the distance threshold comprises a reference distance determined based at least in part on the plurality of training data samples.
 14. A non-transitory computer-readable medium comprising one or more computer-executable instructions that, when executed by at least one processor of a computing device, cause the computing device to: identify at least one generic file-classification definition deployed in a software product installed on the computing device, the generic file-classification definition comprising a data cluster that: has been trained by a set of known malicious files and a set of known clean files; and has been fit to the set of known malicious files and the set of known clean files by applying a statistical algorithm; wherein the data cluster comprises: a plurality of training data samples; a centroid that: has been calculated by applying the statistical algorithm to the set of known malicious files and the set of known clean files; and represents an approximate center of the set of known malicious files and the set of known clean files; a distance threshold that varies throughout the data cluster; and a non-uniform virtual perimeter created by the distance threshold that varies throughout the data cluster; compute a distance from the centroid of the data cluster to a data sample encountered by the computing device; determine that the distance from the centroid to the data sample is below the distance threshold; classify, in response to determining that the distance from the centroid to the data sample is below the distance threshold, the data sample as malware based at least in part on the distance from the centroid to the data sample; query at least one verification server in an attempt to verify the correctness of the malware classification of the data sample by obtaining information about the data sample that was unavailable when the generic file-classification definition was released, the information indicating that the data sample is clean even though the generic file-classification definition classified the data sample as malware; determine, based at least in part on the information about the data sample that was unavailable when the generic file-classification definition was released, that the malware classification of the data sample is incorrect; and modify, in response to the determination that the malware classification of the data sample is incorrect, the generic file-classification definition deployed in the software product based at least in part on the data sample to account for the incorrect classification of the data sample without retraining the generic file-classification definition, wherein modifying the generic file-classification definition comprises: customizing the generic file-classification definition to the computing device based at least in part on browsing behavior or patterns of a user of the computing device and due at least in part to the computing device having encountered the data sample that was incorrectly classified; and decreasing the distance threshold such that: the distance from the centroid to the data sample is above the distance threshold; and the generic file-classification definition would classify the data sample as clean in the event that the generic file-classification definition were to classify the data sample anew. 